Diffusion On Syntax Trees For Program Synthesis (2405.20519v1)

Abstract: LLMs generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program's output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data. To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free grammar. Similar to image diffusion models, our method also inverts ``noise'' applied to syntax trees. Rather than generating code sequentially, we iteratively edit it while preserving syntactic validity, which makes it easy to combine this neural model with search. We apply our approach to inverse graphics tasks, where our model learns to convert images into programs that produce those images. Combined with search, our model is able to write graphics programs, see the execution result, and debug them to meet the required specifications. We additionally show how our system can write graphics programs for hand-drawn sketches.

• The paper presents a novel diffusion model that iteratively refines syntax trees, enhancing program synthesis with built-in runtime debugging.
• It leverages context-free grammars to add and remove noise, enabling efficient exploration and correction in code generation.
• Application in inverse graphics shows significant gains in compilation efficiency, promising more robust and reliable automated code generation.

Diffusion On Syntax Trees For Program Synthesis

Introduction

The paper "Diffusion On Syntax Trees For Program Synthesis" addresses significant challenges in the domain of program synthesis, particularly the limitations of autoregressive models like LLMs that generate code sequentially without the ability to observe and correct the output dynamically. This paper introduces a novel approach by leveraging neural diffusion models that operate on syntax trees, directly addressing issues inherent to the traditional sequential generation of code and providing a method to iteratively refine programs while maintaining syntactic validity.

Methodology

Neural Diffusion on Syntax Trees

The authors propose a neural diffusion model tailored to operate on syntax trees defined by context-free grammars. This fundamentally differs from autoregressive models by iteratively editing code in a manner akin to diffusion models used in image generation. The process involves adding "noise" to the syntax trees and then training the model to denoise or correct these trees, effectively refining the code iteratively.

A critical aspect of this methodology is its ability to observe the runtime output of the programs and make adjustments accordingly, imitating a debugging process. By combining this iterative refinement with search strategies, the model can explore potential program solutions more efficiently.

Inverse Graphics Tasks

The paper demonstrates the application of the proposed approach in the domain of inverse graphics, where the model learns to generate programs that create images matching given inputs. This domain is particularly suitable because small changes in code result in perceptually meaningful changes in the rendered images. The model can thus write graphics programs, execute them, and iteratively debug to achieve the desired output.

Experimental Validation

The efficacy of the approach is validated using various domain-specific languages for drawing images, such as CSG2D and TinySVG, along with a sketch-based extension, CSG2D-Sketch. The results indicate significant improvements over baseline methods, particularly in terms of the number of compilations required to achieve the desired output. The model's ability to iteratively observe and correct program outputs presents a clear advantage in program synthesis.

Key Contributions

1. Diffusion Model for Syntax Trees: A novel application of diffusion models to syntax trees, allowing iterative refinement while preserving syntactic validity.
2. Inverse Graphics Implementation: Deployment of the model for inverse graphics tasks, showcasing its practical utility in converting images into corresponding programs.
3. Enhanced Search Capabilities: Integration of a value model with the diffusion process to guide the search towards more promising program configurations effectively.

Implications and Future Directions

The paper's approach has dual implications:

• Practical Implications: The capability to iteratively refine programs while observing runtime outputs can significantly augment the efficiency of automatic code generation tools, making them more robust and reliable.
• Theoretical Implications: The successful application of diffusion models to discrete and structured data like syntax trees opens new avenues for research in generative models for code and other discrete domains.

Future developments could involve extending this approach to support more complex programming constructs, such as variable bindings, loops, and continuous parameters. Additionally, training the model on large-scale datasets derived from real-world code repositories could enhance its capabilities and generalizability. Applying this iterative refinement methodology to other domains beyond inverse graphics, such as symbolic mathematics or automated theorem proving, also presents an exciting research direction.

Conclusion

The paper "Diffusion On Syntax Trees For Program Synthesis" presents a significant advancement by introducing neural diffusion models to the field of program synthesis. By enabling iterative edits and leveraging runtime feedback, the proposed approach overcomes critical limitations of traditional autoregressive models. The adoption of this method for inverse graphics tasks demonstrates its practical value and sets the stage for broader applications and future improvements in machine-assisted programming.

References

• Kapur, S., Jenner, E., & Russell, S. "Diffusion On Syntax Trees For Program Synthesis."
• Relevant literature cited in the original paper including works on diffusion models, neural program synthesis, and inverse graphics tasks.